data-analysis
japronto
| data-analysis | japronto | |
|---|---|---|
| 6 | 3 | |
| 44 | 8,623 | |
| - | - | |
| 7.3 | 0.0 | |
| 10 months ago | 9 months ago | |
| Jupyter Notebook | C | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
data-analysis
- Why a public database of hospital prices doesn't exist yet
-
Open Database of Hospital Prices
https://github.com/dolthub/data-analysis/tree/main/transpare...
-
Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
Absolutely interested, on my end at least. I wrote this to manage the transparency in coverage files: https://github.com/dolthub/data-analysis/tree/main/transpare... but I'm always looking for better techniques.
Oh wow, I see you used it on those exact files. How about that.
- Healthcare datasets with multiple continuous variables
-
Beyond the trillion prices: pricing C-sections in America
Details: data repository, code repository, and notebook. The linked GitHub repo gives you the tools you need to reproduce this analysis or create your own.
- I wrote some tools to find the prices of C-sections in America. Context in README
japronto
-
Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
100x faster than FastAPI seems easy. I wonder how it compares to other fast Python libraries like Japronto[1] and non-Python ones too.
1 - https://github.com/squeaky-pl/japronto
-
A Look on Python Web Performance at the end of 2022
The source code from the project resides in the github, with more than 8.6k stars and 596 forks is a very popular github, but no new releases are made since 2018, looks pure much not maintained anymore, no PR's are accepted no Issues are closed, still without windows or macOS Silicon, or PyPy3 support. Japronto it self uses uvloop with more than 9k stars and 521 forks and different from japronto is seems to be well maintained.
- Screaming-fast, scalable, asynchronous Python 3.5 HTTP toolkit
What are some alternatives?
json_benchmark - Python JSON benchmarking and "correctness".
socketify.py - Bringing Http/Https and WebSockets High Performance servers for PyPy3 and Python3
synthea - Synthetic Patient Population Simulator
vibora - Fast, asynchronous and elegant Python web framework.
simdjson-go - Golang port of simdjson: parsing gigabytes of JSON per second
yyjson - The fastest JSON library in C
jsplit - A Go program to split large JSON files into many jsonl files
oha - Ohayou(おはよう), HTTP load generator, inspired by rakyll/hey with tui animation.
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
typedload - Python library to load dynamically typed data into statically typed data structures
json-buffet